Integration of Grid Building Technology and Intelligent Microgrid: Innovative Practice in the New Power System

Introduction: Technology Integration and System Upgrade under Energy Transition

Driven by the global "dual carbon" goal, the energy system is undergoing a profound transformation from centralized to distributed, and from single energy to multi energy complementarity. The high proportion of new energy access brings challenges to the stability of the power grid, forcing energy management technology to evolve towards intelligent and proactive support. As a solution for smart energy management in enterprise microgrids, the Ankerui EMS3.0 system has built an energy ecosystem that integrates the Internet of Things, big data, cloud computing, and AI algorithms to achieve full cycle collaboration of "source grid load storage charging". Its core value lies in:

Technology integration: Combining the active support capability of grid building technology with the distributed resource integration capability of smart microgrids to solve the challenges of new energy consumption and grid resilience;

Scenario adaptation: covering diverse scenarios such as industrial parks, commercial complexes, highways, data centers, etc., providing customized energy management strategies;

Win win situation for economy and environmental protection: By means of peak shaving and valley filling, demand control, carbon emission management, etc., the energy consumption cost of enterprises can be reduced by 15% -30%, helping to achieve carbon neutrality goals.

This article will take the Ankerui EMS3.0 system as a case study to analyze the innovative practice of integrating grid construction technology with intelligent microgrids, and explore its value in the new power system.

1 Ankerui EMS3.0 System: Technical Architecture and Core Functions

1.1 Hierarchical distributed architecture: building a "cloud edge end" collaborative system

EMS3.0 adopts an open layered distributed network structure, covering the device layer, transport layer, data layer, and application layer, to achieve full chain closed-loop management of energy data:

Equipment layer:Deploy multifunctional power meters, photovoltaic monitoring devices, energy storage PCS, charging piles, smart meters, etc., covering multiple energy types such as electricity, water, gas, and heat, supporting communication protocols such as Modbus and IEC 60870, and compatible with over 90% of industrial equipment.

Transport layer:Local pre-processing and encrypted transmission of data are realized through edge computing gateways (such as ANet intelligent communication management machine), support breakpoint continuous transmission and power loss alarm, and ensure data stability.

Data layer:Based on cloud computing platforms, real-time/historical data is stored, and API interfaces are provided to seamlessly integrate with third-party systems (ERP, MES, IBMS), breaking down data silos.

Application layer:Integrate modules such as power generation forecasting, economic dispatch, carbon emission management, intelligent operation and maintenance, and dynamically optimize energy strategies through AI algorithms.

1.2 Core function: Full lifecycle management from monitoring to optimization

Power generation prediction and consumption: Based on machine learning algorithms, combined with historical data and meteorological information, predict photovoltaic power generation and dynamically adjust power generation plans. For example, in a project of an energy group in Zhejiang, the system is connected to three photovoltaic inverters to achieve real-time collection of power generation data, and the photovoltaic consumption rate is increased to 85%.

Economic configuration:By optimizing algorithms, provide enterprises with optimization solutions for the ratio of photovoltaic installed capacity to energy storage.someThe highway wind solar energy storage integration project reduces peak load by more than 30% through dynamic energy storage charging and discharging strategies, saving over 600000 yuan in electricity bills annually.

Flexible expansion and demand control:The energy storage system charges during low electricity prices and discharges during peak hours, reducing the basic electricity costs for enterprises. A battery factory project achieved a 30% increase in electrical reliability and an 80% reduction in transformer overload risk through EMS3.0.

Multi strategy staggered charging:Support modes such as "first in, first charge", "bidding strategy", and "invitation strategy" to optimize charging load. A project in Zhejiang implemented time-sharing scheduling for 2 car charging stations and 3 electric scooter charging stations, reducing charging costs by 25%.

Bidirectional energy interaction:Support V2G (Vehicle to Grid) technology for electric vehicles, with low valley charging and peak reverse discharge to enhance energy utilization flexibility. The system aggregates distributed resources to form a virtual power plant and participates in grid peak shaving auxiliary services, which is expected to increase overall revenue by 15% -20%.

II Software system feature interface: concise and clear, comprehensive functions, beautiful and intelligent

2.1 Visible and manageable energy panorama

Real time monitoring of 68 key parameters including mains power, photovoltaic, and energy storage, dynamically presenting the energy topology network, and achieving load forecasting accuracy of up to 9%5%！

3D modeling+GIS geographic information brings energy data to life, allowing mobile phones/computers to control the global situation anytime and anywhere.

2.2 Intelligent optimization ensures that every kilowatt hour of electricity is fully utilized

Peak shaving and valley filling: Energy storage devices charge during low electricity prices and discharge during high electricity prices. A case study of a photovoltaic power station shows that electricity bills have been reduced by 20%!

Flexible expansion: When electricity is overloaded, the energy storage system responds to discharge in seconds, avoiding the risk of transformer overload. The peak load of a certain high-speed project is reduced by 30%!

2.3 Carbon energy synergy, green transformation 'evidence-based'

Built in carbon emission accounting model, automatically generates reports, increases photovoltaic consumption rate to 85% in a semiconductor factory, benchmarking carbon emission intensity against industry benchmarks!

2.4 Security defense,All-timeGuarding the 'lifeline' of energy

Electrical fire warning responsequicklyThe islanding operation mode ensures that critical loads are not powered off, surge protection equipment is diagnosed online, and operation and maintenance efficiency is increased by three times!

2.5 Virtual power plant, turning energy into money

Aggregate photovoltaic, energy storage, and charging pile resources, participate in grid peak shaving or electricity market trading. In a case study of an energy group, the comprehensive energy consumption cost of users decreased by 18% -25%!

III Technological Integration: Collaborative Innovation of Network Building Technology and Intelligent Microgrid

3.1 Hierarchical collaborative control architecture: achieving dynamic balance between source network load and storage

The system adopts a five tiered collaborative model of "province region distribution platform household", coveringMulti level power gridFor example, the "diamond shaped" distribution network in Xiamen can improve the reliability of power supply through this architectureSignificantly improveReduced fault isolation and reconstruction time to millisecondslevel.

In the industrial park scenario, the system predicts photovoltaic power generation and electricity load 72 hours in advance, and dynamically adjusts energy storage charging and discharging strategies. According to project data from a certain energy group in Zhejiang, EMS3.0 has increased the photovoltaic consumption rate to 85% and reduced electricity expenses by about 20%.

3.2 Multi energy complementary algorithm: optimizing distributed resource allocation

The system integrates resources such as photovoltaics, energy storage, charging stations, and controllable loads to build a virtual power plant (VPP). Through game theory power coordination allocation technology, flexible interaction between distributed energy and charging facilities can be achieved. For example, aggregating resources from 50 industrial parks to participate in electricity market transactions is expected to increase overall revenue by 15% -20%.

Support charging modes such as "first in, first charge", "bidding strategy", and "invitation strategy" to optimize electricity load. In a project in Zhejiang, the system implemented time-sharing scheduling for 2 car charging stations and 3 electric vehicle charging stations, reducing peak load by more than 30%.

4 Featured hardware products: providing data support and security assurance

4.1 Monitoring instrument products

4.2 Protection of Measurement and Control Products

4.3 Power Quality Products

Conclusion:

The practice of Ankerui EMS3.0 system has proved that the integration of network construction technology and smart microgrid is not only a technological breakthrough, but also a reconstruction of the underlying logic of the energy system. It simulates the self-organizing characteristics of nature and endows the power grid with a life like ability of "perception decision execution", making the energy system more resilient and elastic in dealing with uncertainty. In the future, with the further penetration of AI algorithms, digital twins, and blockchain technology, the EMS3.0 system will promote the evolution of microgrids towards "zero carbon, autonomous, and open" direction, providing a "Chinese solution" for global energy transformation and helping to build a clean, low-carbon, safe, and efficient modern energy system.